{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T23:55:16Z","timestamp":1780098916649,"version":"3.54.0"},"reference-count":37,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T00:00:00Z","timestamp":1681948800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Introduction<\/jats:title>\n<jats:p>Pre-eclampsia (PE) is a leading cause of perinatal morbidity and mortality worldwide. Low-dose aspirin can prevent PE in high risk pregnancies if started early. However, despite intense research into the area, early pregnancy screening for PE risk is still not a routine part of pregnancy care. Several studies have described the application of artificial intelligence (AI) and machine learning (ML) in risk prediction of PE and its subtypes. A systematic review of available literature is necessary to catalogue the current applications of AI\/ML methods in early pregnancy screening for PE, in order to better inform the development of clinically relevant risk prediction algorithms which will enable timely intervention and the development of new treatment strategies. The aim of this systematic review is to identify and assess studies regarding the application of AI\/ML methods in early pregnancy screening for PE.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Methods<\/jats:title>\n<jats:p>A systematic review of peer-reviewed as well as the pre-published cohort, case-control, or cross-sectional studies will be conducted. Relevant information will be accessed from the following databases; PubMed, Google Scholar, Scopus, Embase, Web of Science, Cochrane Library, Arxiv, BioRxiv, and MedRxiv. The studies will be evaluated by two reviewers in a parallel, blind assessment of the literature, a third reviewer will assess any studies in which the first two reviewers did not agree. The free online tool Rayyan, will be used in this literature assessment stage. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 checklist will be used to guide the review process and the methods of the studies will be assessed using the Newcastle-Ottawa scale. Narrative synthesis will be conducted for all included studies. Meta-analysis will also be conducted where data quality and availability allow.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Ethics and dissemination<\/jats:title>\n<jats:p>The review will not require ethical approval and the findings will be published in a peer-reviewed journal using the PRISMA guidelines.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Trial registration<\/jats:title>\n<jats:p>Trial registration: The protocol for this systematic review has been registered in PROSPERO [<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/www.crd.york.ac.uk\/prospero\/display_record.php?ID=CRD42022345786\" xlink:type=\"simple\">CRD42022345786<\/jats:ext-link>]. <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/www.crd.york.ac.uk\/prospero\/display_record.php?ID=CRD42022345786\" xlink:type=\"simple\">https:\/\/www.crd.york.ac.uk\/prospero\/display_record.php?ID=CRD42022345786<\/jats:ext-link>.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0272465","type":"journal-article","created":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T18:02:26Z","timestamp":1682013746000},"page":"e0272465","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":12,"title":["The use of artificial intelligence and machine learning methods in early pregnancy pre-eclampsia screening: A systematic review protocol"],"prefix":"10.1371","volume":"18","author":[{"given":"Paula L.","family":"Hedley","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian 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The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses Ottawa, Canada: Ottawa Hospital Research Institute; 2019 [cited 2020 20\/01\/2020]. http:\/\/www.ohri.ca\/programs\/clinical_epidemiology\/oxford.asp."},{"issue":"1","key":"pone.0272465.ref034","doi-asserted-by":"crossref","first-page":"e0147601","DOI":"10.1371\/journal.pone.0147601","article-title":"Panethnic Differences in Blood Pressure in Europe: A Systematic Review and Meta-Analysis","volume":"11","author":"PA Modesti","year":"2016","journal-title":"PLoS One"},{"issue":"11","key":"pone.0272465.ref035","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1016\/j.jclinepi.2014.11.023","article-title":"Grading the strength of a body of evidence when assessing health care interventions: an EPC update","volume":"68","author":"ND Berkman","year":"2015","journal-title":"J Clin Epidemiol"},{"issue":"1","key":"pone.0272465.ref036","doi-asserted-by":"crossref","first-page":"e44","DOI":"10.1097\/AOG.0000000000002708","article-title":"ACOG Committee Opinion No. 743: Low-Dose Aspirin Use During Pregnancy","volume":"132","year":"2018","journal-title":"Obstet Gynecol"},{"issue":"11","key":"pone.0272465.ref037","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/ac678a","article-title":"Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency","volume":"67","author":"A Barragan-Montero","year":"2022","journal-title":"Phys Med Biol"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0272465","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T18:02:46Z","timestamp":1682013766000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0272465"}},"subtitle":[],"editor":[{"given":"Ruxandra","family":"Stoean","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2023,4,20]]},"references-count":37,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,4,20]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0272465","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,20]]}}}